图像组转置训练及非凸约束的去噪去模糊算法

doi:10.16451/j.cnki.issn1003-6059.201910006

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Abstract Aiming at insufficient sampling of image patches in the process of over-complete dictionary training of sparse representation model, an algorithm of image patch transform training and non-convex regularization for image denoising and deblurring is proposed. The image patch search strategy with inter-group variance constraint is adopted, and the selected dictionary set is transposed and learned according to the adaptive soft threshold. The l_p(0<p<1) norm is adopted in the reconstruction process to ensure strong sparsity of the results. Split Bregman method is employed to solve the proposed non-convex model. Experimental results show that the proposed algorithm produces better visual effect and Denoising and Deblurring effect.

Key words： Denoising Deblurring Transform Learning Dictionary Learning Non-convex Optimization Bregman Split Iteration

Received: 14 March 2019

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61602413,61873240), General Program of Natural Science Foundation of Zhejiang Province(No.LY19F030016)

Corresponding Authors: WANG Wanliang, Ph.D., professor. His research interests include artificial intelligence.

About author:: YANG Ping, Ph.D. candidate. His resear-ch interests include machine learning and pa-ttern recognition;ZHAO Yanwei, Ph.D., professor. Her research interests include optimized scheduling;ZHENG Jianwei, Ph.D., associate profe-ssor. His research interests include pattern recognition and computer vision.

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Cite this article:

YANG Ping,ZHAO Yanwei,ZHENG Jianwei等. Image Patch Transform Training and Non-convex Regularization for Image Denoising and Deblurring[J]. , 2019, 32(10): 917-926.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201910006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I10/917

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